Experimental results based on offline processing reported at optical conferences
increasingly rely on neural network-based equalizers for accurate data recovery. However …

Hardware implementation of neural network based nonlinear equalizers will encounter
tremendous challenges due to a high-throughput data stream and high computational …

The deployment of artificial neural networks-based optical channel equalizers on edge-
computing devices is critically important for the next generation of optical communication …

This paper introduces a novel methodology for developing low-complexity neural network
(NN) based equalizers to address impairments in high-speed coherent optical transmission …

With the rapid development of machine learning technologies in recent years, different types
of neural network (NN)-based equalizers have been proposed and proved to be efficient …

The computational complexity and system bit-error-rate (BER) performance of four types of
neural-network-based nonlinear equalizers are analyzed for a 50-Gb/s pulse amplitude …

This paper performs a detailed, multi-faceted analysis of key challenges and common
design caveats related to the development of efficient neural networks (NN) based nonlinear …

We present the results of the comparative performance-versus-complexity analysis for the
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …

In this work, we demonstrate the offline FPGA realization of both recurrent and feedforward
neural network (NN)-based equalizers for nonlinearity compensation in coherent optical …

Nowadays, Neural network (NN) has been proved to be an effective solution for nonlinear
equalization in short reach optical systems. However, recent research has mainly focused …